Related papers: Mind the Prompt: A Novel Benchmark for Prompt-based Class-Agnostic Counting

Mind the Prompt: A Novel Benchmark for Prompt-based Class-Agnostic Counting

URL: http://arxiv.org/abs/2409.15953v1
Date: Tue, 24 Sep 2024 10:35:42 GMT
Title: Mind the Prompt: A Novel Benchmark for Prompt-based Class-Agnostic Counting
Authors: Luca Ciampi, Nicola Messina, Matteo Pierucci, Giuseppe Amato, Marco Avvenuti, Fabrizio Falchi,
Abstract summary: Class-agnostic counting (CAC) is a recent task in computer vision that aims to estimate the number of instances of arbitrary object classes never seen during model training. We introduce the Prompt-Aware Counting benchmark, which comprises two targeted tests, each accompanied by appropriate evaluation metrics.
Score: 8.000723123087473
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Class-agnostic counting (CAC) is a recent task in computer vision that aims to estimate the number of instances of arbitrary object classes never seen during model training. With the recent advancement of robust vision-and-language foundation models, there is a growing interest in prompt-based CAC, where object categories to be counted can be specified using natural language. However, we identify significant limitations in current benchmarks for evaluating this task, which hinder both accurate assessment and the development of more effective solutions. Specifically, we argue that the current evaluation protocols do not measure the ability of the model to understand which object has to be counted. This is due to two main factors: (i) the shortcomings of CAC datasets, which primarily consist of images containing objects from a single class, and (ii) the limitations of current counting performance evaluators, which are based on traditional class-specific counting and focus solely on counting errors. To fill this gap, we introduce the Prompt-Aware Counting (PrACo) benchmark, which comprises two targeted tests, each accompanied by appropriate evaluation metrics. We evaluate state-of-the-art methods and demonstrate that, although some achieve impressive results on standard class-specific counting metrics, they exhibit a significant deficiency in understanding the input prompt, indicating the need for more careful training procedures or revised designs. The code for reproducing our results is available at https://github.com/ciampluca/PrACo.